This chapter will give a high level overview about Amazon SageMaker,
in-depth tutorials can be found on the Sagemaker
website.

SageMaker offers Jupyter notebooks and supports MXNet out-of-the box.
You can run your notebooks on CPU instances and as such profit from the
free tier. However, more powerful CPU instances or GPU instances are
charged by time. Within this notebook you can fetch, explore and
prepare training
data.

Once the data is ready, you can easily launch training via the SageMaker
SDK. So there is no need to manually configure and log into EC2
instances. You can either bring your own model or use SageMaker’s
built-in
algorithms
that are tailored to specific use cases such as computer vision, NLP
etc. SageMaker encapsulates the process of training into the class
Estimator and we can now start the training on the local notebook
instance:

If you require a more powerful platform for training, then you only need
to change the train_instance_type. Once you call fit, SageMaker will
automatically create the required EC2 instances, train your model within
a Docker container and then immediately shutdown these instances.
Fit() requires an entry point (here train.py) that describes the
model and training loop. This script needs to provides certain
functions, that will be automatically called by SageMaker once you train
and deploy the model. More information about the entry point script can
be found
here.
When the model is ready for deployment you can use SageMaker’s hosting
services
that create an HTTPS endpoint where model inference is provided.